Breast cancer is a very heterogeneous disease (The Cancer Genome Atlas Network, et al., 2012). In the past, validated clinicopathologic prognostic factors, such as tumor size, lymph node involvement, histologic grade, and age have been widely used by clinicians to guide treatment decisions. This approach resulted in significant numbers of over-treated and undertreated patients. It is well known that patients with similar pathological characteristics can have very different responses toward certain therapies, although the mechanisms of such responses have been poorly understood. More recently, evaluation of the status of estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2) gene, and progesterone receptor (PR) has become routine practice because each has been validated as a prognostic marker. The development of high-throughput genomics technologies such as microarrays and next generation sequencing has allowed more personalized cancer therapy (PCT) based on patients' genomic profiles (Oakman, et al., 2010, Dotan, et al., 2010, Eng-Wong, et al., 2010). The genomic information obtained using these technologies can be much better predictors of treatment responses than the commonly used clinical variables. In PCT, a set of genetic markers from the large volume of genomic information needs to be carefully selected, which is often combined with clinical information, to build models to predict the likely outcome of a patient's current standing or response to a particular treatment. For chemotherapy, two decisions need to be made: whether chemotherapy should be received and, if so, which chemotherapy should be received. Both decision making steps can potentially benefit from PCT. Many studies have found gene signatures for predicting overall survival or recurrence of breast cancer (van de Vijver, et al., 2002, Paik, et al., 2006, Wang, et al., 2005, van't Veer, et al., 2002, Mook, et al., 2007, Strayer, et al., 2010, Buyse, et al., 2006, Foekens, et al., 2006, Look, et al., 2002, Harbeck, et al., 2013), which can be used to provide guidance on whether a more aggressive treatment strategy, such as chemotherapy, should be taken. For example, ONCOTYPE DX, a commercially available diagnostic test based on the expression of a 21-gene panel, has been widely used in the prognosis of breast cancer. Studies have also been performed to predict responses for a particular type of treatment or for a population with mixed treatments without stratification by treatment types (Hatzis, et al., 2011, Graeser, et al., 2010, Shen, et al., 2012, Esserman, et al., 2012, Miyake, et al., 2012, Lips, et al., 2012, Hess, et al., 2006, Takada, et al., 2012, Albain, et al., 2010, Liu, et al., 2012). No studies in the past has developed a personalized treatment strategy to select among multiple chemotherapy regimens. When chemotherapy is to be received, patients still lack guidance on which regimen is the most effective for them. An interesting and important problem, which few studies in the past have addressed, is how much PCT can benefit patients when they decide to receive one of the currently available regimens. In principle, if all patients respond similarly to currently available regimens, then PCT will not be useful at present, although it may become useful when new treatments are introduced. Another challenge is, given a significant number of patients who respond differently to at least two regimens (those who can benefit from PCT), identifying and assigning those patients to the most effective regimen. Hence, there is a need to develop improved methods for selecting suitable and effective chemotherapy regimens for breast cancer patients.